Practice question · Multiple choice
A population is 60% women and 40% men. Simple random sampling could, by chance, produce a sample that is 75% women. Stratified sampling cannot. Why is removing that possibility worth giving up pure randomness for?
Hints
- Random sampling gets the proportions right on average. Ask what "on average" leaves open for any one sample.
- What do you know before sampling that stratification lets you use?
Show the answer
C. Because stratification guarantees the sample matches on a key variable.
Why
Random sampling is unbiased in the long run and you only get one sample, which can still land at 75%. Stratification builds a fact you already know into the design so that error cannot occur. Its weakness is variance rather than bias, and it protects only the variables you stratify on, which is a judgement about what matters for the outcome.
Practise Sampling Techniques
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